| NSF Org: |
OISE Office of International Science and Engineering |
| Recipient: |
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| Initial Amendment Date: | August 28, 2013 |
| Latest Amendment Date: | August 28, 2013 |
| Award Number: | 1338378 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Anne Emig
OISE Office of International Science and Engineering O/D Office Of The Director |
| Start Date: | October 1, 2013 |
| End Date: | September 30, 2014 (Estimated) |
| Total Intended Award Amount: | $34,598.00 |
| Total Awarded Amount to Date: | $34,598.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
712 BROADWAY ST S MENOMONIE WI US 54751-2458 (715)232-1123 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
AE |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | Catalyzing New Intl Collab |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.079 |
ABSTRACT
1338378
Bae
This project will support a team headed by Dr. Wan Bae, University of Wisconsin-Stout, Menomonie, WI for a two week visit to the United Arab Emirates (UAE) for the establishment of new international research collaborations between researchers in the two countries. This visit will enable the PI and Dr. Cheng Liu and two undergraduate students from the UWI-Stout, Dr. Petr Vojtchovsky from the University of Denver, and Dr. Shashi Shekhar from the University of Minnesota, to meet with Dr. Shayma Alkobaisi, Dr. Ahmed Al Faresi, Dr. Mohammad Masud, Dr. Fatma Maskari and their students from the United Arab Emirates University, and Dr. Ibrahim Kamel from the University of Sharjah to develop a research framework for modeling and analysis of individual exposure to various environmental conditions. The research will focus on developing data models and computing algorithms for effectively mapping individuals? environmental exposure to their health conditions, and implementing Map/Reduce methods for efficiently processing iterative computations of the proposed models and algorithms. As a result, the research team will submit a subsequent grant proposal targeted for the NSF Smart Health and Wellbeing (SHB) program.
Intellectual Merit: Relations between negative health effects like asthma and lung cancer and elevated levels of the environmental factors, such as air pollution, tobacco smoke and humidity, have been detected in several large scale exposure studies. Evaluating environmental exposures often requires the ability to track, monitor, store, and analyze individual moving trajectories along with several environmental conditions the individual is exposed to in order to identify relationships among these data. Challenges arise due to spatio-temporal uncertainty, data size, and iterative computations of commonly used data modeling algorithms such as the Back propagation neural network algorithm. The main objectives of this research are: (1) to develop novel data models to map individuals environmental exposures to health levels, (2) to design a new technique for implementing the proposed models on the Map/Reduce paradigm of the Hadoop system, (3) to develop data analysis algorithms to characterize behaviors in learned models and interpret the data for estimating their effects on human health, (4) to build an evaluation system for Asthma patients as a case study. The research team, consisting of mathematicians, computer and information scientists, engineers and medical expertise, is capable of carrying out the planned tasks.
Broader Impacts: The project will support two U.S. undergraduate students to be actively involved in scientific research. Their involvement is designed to integrate research and education through various activities. Gaining experience with inter-cultural collaboration is one of the mutual benefits to both the U.S. and UAE students. The also promotes diversity with the involvement by students from the U.S. who may be first-generation college students and UAE national students. The U.S. and UAE researchers will build new relationships that are the basis for future collaborations in research and education. Further, this project will broaden the understanding of the impact of the environment on public health and the importance of individual-based health care for patients, doctors, and healthcare providers.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This project supported a two week visit in January, 2014 to the United Arab Emirates (UAE) for the establishment of new international research collaborations between U.S. researchers and researchers in the UAE. This visit will enable US researchers and two undergraduate students to meet with the foreign research collaborators and their students from the United Arab Emirates University and University of Sharjah to develop a research framework for modeling and analysis of individual exposure to various environmental conditions. The research focused on developing data models and computing algorithms for effectively mapping individuals’ environmental exposure to their health conditions, and implementing Map/Reduce methods for efficiently processing iterative computations of the proposed models and algorithms. The main objectives of this visit are, (1) to implement a research framework including system set-up, installation of initial data collection tools for preliminary results, and proofs of concepts, (2) to develop a long-term international collaborative research partnership between the U.S. and UAE in a rapidly growing field of spatio-temporal data mining, and (3) to involve undergraduate students from both the U.S. and UAE in research and educational activities. As a result, the research team will submit a subsequent grant proposal targeted for the NSF Smart and Connected Health (SCH) program.
Exposure to environmental risk factors as well as weather conditions are known to have negative effects on health. Until recently, there was little a society could do for an individual at risk, other than provide general warnings when the concentration of pollutants or weather conditions deviated from the norm. Similarly, the assessment of individuals’ exposure over time has been confined to population and geographic averages, rather than individualized estimates. Recent advances in sensors and mobile technology have enabled real-time measurements of environmental variables and, at the same time, provided information about the spatio-temporal behavior of individuals. This can dramatically change the way health and wellness are assessed as well as how care and treatment are delivered. Together with just-in-time adaptive intervention, this information will transform our ability to accurately assess individuals’ exposure and open the possibility of real-time intervention to minimize individuals’ exposures to environmental triggers. What is missing to achieve this vision are computational models and algorithms that would, in combination with behavioral models, convert raw data collected over time and in real-time to optimal estimates of exposure and then to optimal intervention. The development of these computational algorithms requires the development of probabilistic structures that combine diverse measurements with behavioral assessment.
Intellectual merit: The transformative innovation in this research is the development of a new theoretical and computational framework for exposure measurement, estimation, evaluation and prediction through the development of exposure uncertainty models and behavioral models and spatial join algorithms in the presence of uncertainty. The main objectives of this research are to develop: (1) mathematical models for accurately estimating exposure in the presence of uncertainty, (2) probabilistic multi-layer spatial join algorithms that integrate spatio-temporal datasets, and (3) computing algorithms that overlay individuals’ uncertain trajectories over a generated heat map of multiple environmental data with mobility estimates derived from behavioral data. Supplemental tasks comprise: (a) development of mobile-phone sensors for measurement of environmental variables and (b) design of hardware and software architecture for analysis and storage of d...
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